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Machine Learning was born from pattern recognition, but today it allows us to develop applications that improve their performance by «learning» from data collected in past situations. In this Python specialisation you will be able to apply Machine Learning to real projects, including preparation and related tasks, deployment in production and the lifecycle of a model.
Unit 1: Introduction to Big Data and Machine Learning
Unit 2: Work environment
Unit 3: Python and Scikit-learn numeric libraries
Unit 1: Linear regression
Unit 2: Gradient descent optimisation
Unit 3: Standardisation, regularisation and validation
Unit 4: Bayesian models and model evaluation
Unit 5: Classification
Unit 6: Introduction to neural networks
Unit 1: Optimisation by randomisation
Unit 2: Clustering
Unit 1: Anomalies detection
Unit 2: Recommendation systems
Unit 3: Genetic algorithms
Unit 1: ML systems approach
Unit 2: Feature engineering
Unit 3: Principal Components Analysis (“PCA”)
Unit 4: Assemblies
Unit 5: Models’ evaluation and improvement
Unit 6: Operations in ML
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